ArticlePhysical review. E2018
Variational encoding of complex dynamics.
Article in Physical review. E, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
47 citing papers in PubMed.
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Latent thermodynamic flows: unified representation learning and generative modeling of temperature-dependent behaviors from limited data.Chemical science · 2026Article
- Random Functions as Data Compressors for Machine Learning of Molecular Processes.Journal of chemical theory and computation · 2026Article
- Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications.Chemical reviews · 2026Review
- A beginner's approach to deep learning applied to VS and MD techniques.Journal of cheminformatics · 2025Review
- Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics.The Journal of chemical physics · 2025Article
- Article
- Information Bottleneck Approach for Markov Model Construction.Journal of chemical theory and computation · 2024Article
- Article
- Enhanced Sampling with Machine Learning.Annual review of physical chemistry · 2024Review
- Tutorial on how to build non-Markovian dynamic models from molecular dynamics simulations for studying protein conformational changes.The Journal of chemical physics · 2024Article
- Markov State Models: To Optimize or Not to Optimize.Journal of chemical theory and computation · 2024Article
- Uncertainties in Markov State Models of Small Proteins.Journal of chemical theory and computation · 2023Article
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
- An Efficient Path Classification Algorithm Based on Variational Autoencoder to Identify Metastable Path Channels for Complex Conformational Changes.Journal of chemical theory and computation · 2023Article
- From Deep Mutational Mapping of Allosteric Protein Landscapes to Deep Learning of Allostery and Hidden Allosteric Sites: Zooming in on "Allosteric Intersection" of Biochemical and Big Data Approaches.International journal of molecular sciences · 2023Review
- Machine learning and protein allostery.Trends in biochemical sciences · 2023Review
- Exploring and Learning the Universe of Protein Allostery Using Artificial Intelligence Augmented Biophysical and Computational Approaches.Journal of chemical information and modeling · 2023Review
- Predicting efficacy of drug-carrier nanoparticle designs for cancer treatment: a machine learning-based solution.Scientific reports · 2023Article
- Chasing collective variables using temporal data-driven strategies.QRB discovery · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Often the analysis of time-dependent chemical and biophysical systems produces high-dimensional time-series data for which it can be difficult to interpret which individual features are most salient. While recent work from our group and others has demonstrated the utility of time-lagged covariate models to study such systems, linearity assumptions can limit the compression of inherently nonlinear dynamics into just a few characteristic components. Recent work in the field of deep learning has led to the development of the variational autoencoder (VAE), which is able to compress complex datasets into simpler manifolds. We present the use of a time-lagged VAE, or variational dynamics encoder (VDE), to reduce complex, nonlinear processes to a single embedding with high fidelity to the underlying dynamics. We demonstrate how the VDE is able to capture nontrivial dynamics in a variety of examples, including Brownian dynamics and atomistic protein folding. Additionally, we demonstrate a method for analyzing the VDE model, inspired by saliency mapping, to determine what features are selected by the VDE model to describe dynamics. The VDE presents an important step in applying techniques from deep learning to more accurately model and interpret complex biophysics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.